Bibliographic record
Abstract
Books reviewed: M. Petrou and P. Bosdogianni, Image Processing, The Fundamentals S. N. Lane, K. S. Richards and J. H. Chandler, Landform Monitoring, Modelling And Analysis T. M. Lillesand and R. W. Kiefer, Remote Sensing And Image Interpretation A. S. Câmara and J. Raper, Spatial Multimedia And Virtual Reality M. N. Demers, Fundamentals Of Geographic Information Systems J. L. Casanova, Remote Sensing In The 21st Century: Economic And Environmental Applications G. McGrath and L. M. Sebert, Mapping A Northern Land: The Survey Of Canada A. S. Milman, Mathematical Principles Of Remote Sensing: Making Inferences From Noisy Data K. Kraus, Photogrammetrie Band 3: Topographische Informations‐systeme E. M. João, Causes And Consequences Of Map Generalisation R. B. Parry and C. R. Perkins, World Mapping Today P. M. Mather, Computer Processing Of Remotely Sensed Images: An Introduction M. E. Schaepman, Calibration Of A Field Spectroradiometer J. C. Iliffe, Datums And Map Projections For Remote Sensing, Gis And Surveying
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.595 | 0.580 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".